# 引用所需要的库
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.optim as optim  # 优化器
# 过滤警告
import warnings
# 处理时间数据
import datetime
from sklearn import preprocessing

warnings.filterwarnings("ignore")
# %matplotlib inline

# 读取数据
features = pd.read_csv("temps.csv")
# 查看数据的格式
print(features.shape)
print(features.head)

# 数据如下
#       year  month  day   week  temp_2  temp_1  average  actual  friend
# 0    2016      1    1    Fri      45      45     45.6      45      28
# 1    2016      1    2    Sat      44      45     45.7      44      61
# 2    2016      1    3    Sun      45      44     45.8      41      56
# 3    2016      1    4    Mon      44      41     45.9      40      53
# 4    2016      1    5   Tues      41      40     46.0      44      41
# ..    ...    ...  ...    ...     ...     ...      ...     ...     ...
# 343  2016     12   27   Tues      42      42     45.2      47      47
# 344  2016     12   28    Wed      42      47     45.3      48      58
# 345  2016     12   29  Thurs      47      48     45.3      48      65
# 346  2016     12   30    Fri      48      48     45.4      57      42
# 347  2016     12   31    Sat      48      57     45.5      40      57

# year, moth, day, week分别表示的具体的时间
# temp_2：前天的最高温度值
# temp_1：昨天的最高温度值
# average：在历史中，每年这一天的平均最高温度值
# actual：这就是我们的标签值了，当天的真实最高温度
# friend：这一列可能是凑热闹的，你的朋友猜测的可能值，咱们不管它就好了


# 分别得到年月日
years = features['year']
months = features['month']
days = features['day']

# datetime格式
dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]
print(dates)

# 准备画图
# 指定默认风格
plt.style.use('fivethirtyeight')

# 设置布局
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(10, 10))
fig.autofmt_xdate(rotation=45)

# 标签值
ax1.plot(dates, features['actual'])
ax1.set_xlabel('')
ax1.set_ylabel('Temperature')
ax1.set_title('Max Temp')

# 昨天
ax2.plot(dates, features['temp_1'])
ax2.set_xlabel('')
ax2.set_ylabel('Temperature')
ax2.set_title('Previous Max Temp')

# 前天
ax3.plot(dates, features['temp_2'])
ax3.set_xlabel('Date')
ax3.set_ylabel('Temperature')
ax3.set_title('Two Days Prior Max Temp')

# 我的逗逼朋友
ax4.plot(dates, features['friend'])
ax4.set_xlabel('Date')
ax4.set_ylabel('Temperature')
ax4.set_title('Friend Estimate')

plt.tight_layout(pad=2)
plt.show()

# 独热编码
features = pd.get_dummies(features)
print(features)

# 标签
labels = np.array(features['actual'])
# 在特征中去掉标签
features = features.drop('actual', axis=1)
# 名字单独保存一下，以防丢失
feature_list = list(features.columns)
print(feature_list)
# 转化成合适的形式
featuresa = np.array(features)
print(featuresa.shape)

# 标准化处理
input_features = preprocessing.StandardScaler().fit_transform(features)
print(input_features)

# 手动构造网络模型
x = torch.tensor(input_features, dtype=float)
y = torch.tensor(labels, dtype=float)

# 权重参数初始化
weights = torch.randn((14, 128), dtype=float, requires_grad=True)
biases = torch.randn(128, dtype=float, requires_grad=True)
weights2 = torch.randn((128, 1), dtype=float, requires_grad=True)
biases2 = torch.randn(1, dtype=float, requires_grad=True)
learning_rate = 0.001  # 学习率
losses = []

for i in range(1000):
    # 计算隐层
    hidden = x.mm(weights) + biases
    # 加入激活函数
    hidden = torch.relu(hidden)
    # 预测结果
    predictions = hidden.mm(weights2) + biases2
    # 计算损失
    loss = torch.mean((predictions - y) ** 2)  # 均方误差
    losses.append(loss.data.numpy())
    # 打印损失值
    if i % 100 == 0:
        print('loss:', loss)
    # 反向传播计算
    loss.backward()

    # 更新参数
    weights.data.add_(- learning_rate * weights.grad.data)
    biases.data.add_(- learning_rate * biases.grad.data)
    weights2.data.add_(- learning_rate * weights2.grad.data)
    biases2.data.add_(- learning_rate * biases2.grad.data)

    # 每次迭代都得记得清空
    weights.grad.data.zero_()
    biases.grad.data.zero_()
    weights2.grad.data.zero_()
    biases2.grad.data.zero_()

input_size = input_features.shape[1]
hidden_size = 128
output_size = 1
batch_size = 16
my_nn = torch.nn.Sequential(
    torch.nn.Linear(input_size, hidden_size),
    torch.nn.Sigmoid(),
    torch.nn.Linear(hidden_size, output_size),
)
cost = torch.nn.MSELoss(reduction='mean')
optimizer = torch.optim.Adam(my_nn.parameters(), lr=0.001)

# 训练网络
losses = []
for i in range(1000):
    batch_loss = []
    # MINI-Batch方法来进行训练
    for start in range(0, len(input_features), batch_size):
        end = start + batch_size if start + batch_size < len(input_features) else len(input_features)
        xx = torch.tensor(input_features[start:end], dtype=torch.float, requires_grad=True)
        yy = torch.tensor(labels[start:end], dtype=torch.float, requires_grad=True)
        prediction = my_nn(xx)
        loss = cost(prediction, yy)
        optimizer.zero_grad()
        loss.backward(retain_graph=True)
        optimizer.step()
        batch_loss.append(loss.data.numpy())

    # 打印损失
    if i % 100 == 0:
        losses.append(np.mean(batch_loss))
        print(i, np.mean(batch_loss))

x = torch.tensor(input_features, dtype=torch.float)
predict = my_nn(x).data.numpy()

# 转换日期格式
dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]

# 创建一个表格来存日期和其对应的标签数值
true_data = pd.DataFrame(data={'date': dates, 'actual': labels})

# 同理，再创建一个来存日期和其对应的模型预测值
years = features.iloc[:, feature_list.index('year')]
months = features.iloc[:, feature_list.index('month')]
days = features.iloc[:, feature_list.index('day')]

test_dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]

test_dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in test_dates]

predictions_data = pd.DataFrame(data={'date': test_dates, 'prediction': predict.reshape(-1)})

# 真实值
plt.plot(true_data['date'], true_data['actual'], 'b-', label='actual')

# 预测值
plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label='prediction')
plt.xticks(rotation='vertical')
plt.legend()

# 图名
plt.xlabel('Date')
plt.ylabel('Maximum Temperature (F)')
plt.title('Actual and Predicted Values')

plt.show()
